Process control theory establishes methods of observing and correcting variances between desired and actual output. Statistics play a major role in process control, since statistical methods are used to determine acceptable limits and deviations from an ideal average. Engineering processes establish certain quality standards to increase efficiency, create a safe work environment, and ensure product consistency.
Manufacturing environments often set up automated control processes based on process control theory. The foundation of the theory states that quality can be improved by reducing performance inconsistencies through mathematical control methods. A manufacturing facility's management works with a company's executives to determine ideal product attributes, which are used in inspection checkpoints and as measurements of quality. One of the main goals behind process control is to reduce extreme variations within the same finished product.
By seeking to reduce deviations from an established norm, applications of process control theory help increase cost efficiency. Machinery used in the manufacturing of goods can be programmed to produce certain end results and product characteristics, which saves companies time and money. Even though it is not practical to completely automate some production processes, workers can also use statistical process control methods. This is typically seen with random batch inspections of finished products, as quality control teams have to make decisions on whether to modify automation, scrap an entire batch, or allow completed products to go to market.
At times variations in product or performance consistency can be attributed to uncontrollable circumstances. These factors are usually uncovered when sharp deviations occur between desired and actual performance. Since indications of these variances often show up in reported statistics, further investigation typically occurs. Part of process control theory is determining the root cause of inconsistencies and finding probable ways to correct them.
Process control theory recognizes that some circumstances that lead to undesired results are extreme and uncontrollable. In the case of a manufacturing facility, a natural disaster or power outage might qualify as an uncontrollable circumstance behind interrupted production. Practical applications of the theory help managers identify why deviations may be occurring since many causes can be attributed to controllable factors, such as inadequate materials, outdated machinery, incorrect parameters, and poor training methods. One of the important aspects behind process control is establishing acceptable high and low limits.
These limits define an acceptable range of deviation from the ideal average. Most business leaders recognize and accept the fact that there will always be some degree of variance between desired and actual performance. The idea is to reduce the variance as much as possible, most often keeping the quality of all products within two to three standard deviations from the established norm.
